Process robotics data with HPCC Systems cluster on Kubernetes

The proposal application period for 2021 internships is now closed. The proposal period for 2022 internships will open in the Fall.

This project will be completed by a student accepted on to the 2021 HPCC Systems Intern Program.

Student work experience opportunities also exist for students who want to suggest their own project idea. Project suggestions must be relevant to HPCC Systems and of benefit to our open source community.

Find out about the HPCC Systems Summer Internship Program.

Project Description

This will is a continuing work from last year "Process robotics data with HPCC Systems". The main focus will be on HPCC System cluster on Kubernetes, particularly on Microsoft Azure. The project will adopt existing General Neural Network (GNN) model to local and Azure Kubernetes cluster. Some related code and environment may also need be updated for example, latest ROS, Ubuntu 20.04 and potential new TensorFlow release, etc. The student also will help to identify any necessary change or add-on in HPCC-Platform to support Machine Learning on HPCC System Cloud in both local and public cloud such as Azure, AWS and Google Cloud.

If you are interested in this project, please contact Contact Details.

Completion of this project involves:

Learning HPCC System ML GNN
Learning previous Robotics GNN code
Collect or get train data (images)
Load the image data to the cloud
Train the model

By the mid term review we would expect you to have:

Load image data to the cloud
Written initial ECL code
Trained a model with HPCC Systems GNN with some initial result

Mentor	David De Hilster Contact Details Backup Mentor: Xiaoming Wang Contact Details
Skills needed	Docker and Kubernetes Git, CMake Build ROS package HPCC Systems Platform and ECL Python, Unix Bash, Machine Learning, Neural Networks, particularly Convolution Neural Networks (CNN) Kera, TensorFlow Ability to build and test the HPCC system (guidance will be provided). Ability to write test code. Knowledge of ECL is not a requirement since it should be possible to re-use existing code with minimal changes for this purpose. Links are provided below to our ECL training documentation and online courses should you wish to become familiar with the ECL language.
Deliverables	Midterm Load image data to the cloud Written initial ECL code Trained a model with HPCC Systems GNN with some initial result End of project Tuning hyperparameters to improve model training. Documentation A complete github project
Other resources	HPCC Systems website https://hpccros.wordpress.com/ https://github.com/hpcc-systems/GNN https://github.com/hpcc-systems/GPU-Deep-Learning Github repo: https://github.com/Aramist/hpcc-robotics https://hpccrosgnn.wordpress.com/ Hands-on Machine Learning with Scikit-Learn Kera & TensorFlow Learning ECL documentation and on-line training courses.

Process robotics data with HPCC Systems cluster on Kubernetes

Skills needed

Deliverables

Other resources